Volume 37, Number C1, February 2002ECORAD 2001: The Radioecology - Ecotoxicology of Continental and Estuatine Environments
|Page(s)||C1-1139 - C1-1145|
|Published online||25 March 2010|
Data assimilation for the management of environmental and radiological data in case of nuclear accident
Institute of Protection and Nuclear Safety (IPSN), Division of Environmental Protection (DPRE), CE Cadarache, 13108 Saint-Paul-lez-Durance cedex, France
The "Data Assimilation for the Management of Environmental and Radiological Data in Case of Nuclear Accidents" project managed by the IPSN/DPRE started in 2001 for at least three years. It is based on the IPSN skills in the field of data analysis and the modeling of radionuclide transfers in the environment, more precisely along the food chain pathways. The main part of the project consists in defining methods to fit radioecological models with incoming measured values when predictions do not correspond to such measured values. In such a case, the radioecological parameters of the model or some poorly assumed initial conditions are supposed to be responsible and have to be modified. In order to update the modeling, the "parameters" of the model have to be adjusted considering the measured values. This problem of data assimilation can be represented by a nonlinear program (or optimization problem), where the relations between the parameters and their definition domains constitute the set of constraints; then, the cost function to minimize is the difference between predictions and measured values. The experts can control the process of optimization by adding some well chosen constraints expressing their understanding of the phenomena considered in the studied case.
Problems of nonlinear programs are known to be very difficult to solve and the calculation time is rarely reasonable with deterministic methods, which scan all the research space. In this context, it is proposed to treat this problem by using a combination of a deterministic method such as Constraints Satisfaction Problems (CSP) and a stochastic method with a genetic algorithm to reach best performances.
© EDP Sciences, 2002
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